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International Journal for Multiscale Computational Engineering

Publicou 6 edições por ano

ISSN Imprimir: 1543-1649

ISSN On-line: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

HOMOGENIZATION OF FIBER-REINFORCED COMPOSITES WITH RANDOM PROPERTIES USING THE LEAST-SQUARES RESPONSE FUNCTION APPROACH

Volume 9, Edição 3, 2011, pp. 257-270
DOI: 10.1615/IntJMultCompEng.v9.i3.20
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RESUMO

The main issue in this elaboration is computational study of the homogenized elasticity tensor for the periodic random composite using the improved stochastic generalized perturbation technique. The uncertainty of the composite appears at the component's material properties, treated here as the Gaussian random variables, while its micro- and macrogeometry remains perfectly periodic. The effective modules method consisting in the cell problem solution is enriched with the generalized stochastic perturbation method. This method is implemented without the necessity of a large number of increasing order equations. The response function between the homogenized tensor and the input random parameter is determined numerically using several deterministic solutions and the least-squares approximation technique. Since classical polynomial approximation techniques may result in some errors for the lower and upper bound of the input parameter variability set, the least-squares approximation is used, where the degree of an approximant is the additional input variable. This approach has hybrid computational implementation{partially in the homogenization-oriented finite element method code MCCEFF and in the symbolic environment of the MAPLE 13 system, giving a wide range of approximation techniques that can also be modified in a graphical mode.

CITADO POR
  1. References, in The Stochastic Perturbation Method for Computational Mechanics, 2013. Crossref

  2. Pivovarov Dmytro, Zabihyan Reza, Mergheim Julia, Willner Kai, Steinmann Paul, On periodic boundary conditions and ergodicity in computational homogenization of heterogeneous materials with random microstructure, Computer Methods in Applied Mechanics and Engineering, 357, 2019. Crossref

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